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State Estimation and Fault Detection of an Aircraft Using Nonlinear Filters
Author(s) -
Christmann Julia,
Kappenberger Carsten,
Marheineke Nicole
Publication year - 2011
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201110392
Subject(s) - extended kalman filter , particle filter , kalman filter , nonlinear system , fault detection and isolation , fault (geology) , filter (signal processing) , control theory (sociology) , state (computer science) , invariant extended kalman filter , computer science , moving horizon estimation , work (physics) , engineering , algorithm , artificial intelligence , physics , control (management) , mechanical engineering , quantum mechanics , seismology , computer vision , geology
An aircraft is a complex technical system, which has to satisfy high safety standards. The whole system of an aircraft has to be monitored because more and more actions happen automatically. Therefore unpredictable errors should be detected as fast as possible. In this work two different nonlinear filters namely the Extended Kalman Filter (EKF) and the Particle Filter (PF) are studied for the application of fault detection with simultaneous estimations of states. Results show that EKF leads to better approximation for nearly linear problems, while PF yields a more accurate approximation for worst case scenarios. (© 2011 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)